Abstract

Taking into account of the illumination characteristics of nighttime imaging, a new method for nighttime image dehazing is proposed in this paper. In the first place, based on the color transfer theory, the illumination level of nighttime hazy image can be artificially enhanced through flexibly selecting the reference image. In contrast to the classical model of color transfer with the strategy of overall to overall transfer, the modified model focuses on the different characteristics of various regions in the original image, and it works well even though the nighttime image is interfered by various artificial light sources. In the second place, the enhancement dehazing method based on the theory of guided image filtering is adopted since the key parameters of dehazing method using the atmospheric degradation model are difficult to obtain in the conditions of nighttime imaging. In addition, the key model parameters of guided image filter are selected according to the boundary information of original image rather than the original image itself, which makes it more advantageous for dehazing image taken on the hazy night. The experimental results show that the proposed method has better performance than the classical daytime dehazing methods. Additionally, our method exhibits superior effect compared to the well-known nighttime dehazing method in the aspects of suppressing color distortion and background illumination controlling. The evaluations of the experimental results are established on both the subjective and objective aspects, so the conclusion in this paper is more convincing.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 41601353, 61503300 and 61502387), and Foundation of Key Laboratory of Space Active Opto-Electronics Technology of Chinese Academy of Sciences (No. AOE-2016-A02), and Scientific Research Program Funded by Shaanxi Provincial Education Department (No. 16JK1765), and Foundation of State Key Laboratory of Transient Optics and Photonics, Chinese Academy of Sciences (No. SKLST201614), and Natural Science Basic Research Plan in Shaanxi Province of China (No. 2017JQ4003).